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Amazon SageMaker Ground Truth Website Full Guide (2026)

Data labeling service within AWS SageMaker.

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Updated May 26, 2026

screenshot of Amazon SageMaker Ground Truth

Introduction

Amazon SageMaker Ground Truth provides a browser-first interface within the AWS console for orchestrating large-scale data labeling projects essential for training computer vision models. It enables ML practitioners to define annotation tasks, manage labeling workforces, and generate high-quality ground truth datasets for use cases like object detection, image classification, and semantic segmentation, directly leveraging S3 data sources and outputs.

Key Features

Core Capabilities

1

Image and video annotation task setup

2

Private, vendor, and Amazon Mechanical Turk workforce integration

3

Labeling job creation and management console

4

S3 data input/output configuration for dataset

Additional Details

1

Active learning for automated data labeling

2

Customizable labeling instructions editor

3

Consensus-based quality control mechanism

4

Bounding box, polygon, and keypoint annotation tool

Use Cases

For Developers

Training Custom Object Detector

ML engineers use Ground Truth to rapidly annotate large image datasets with bounding boxes for specific objects, generating the ground truth needed to train custom YOLO or Faster R-CNN model

How to Use Amazon SageMaker Ground Truth

Configure a New Labeling Job

Navigate to the SageMaker console, select "Ground Truth" from the left navigation, and choose "Labeling jobs." Click "Create labeling job," specify your S3 input data location, and select the desired computer vision task type (e.g., "Object detection")

Amazon SageMaker Ground Truth Alternatives

Amazon SageMaker Ground Truth Status

Active

Service is operational

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